III: Small: Quantifying Multifaceted Perception Dynamics in Online Social Networks

衡量公众的看法以及它们如何随着时间的推移而变化，是市场营销、公共卫生和政治的核心问题。传统的衡量方法依赖于调查和焦点小组，这可能是昂贵和耗时的。在线社交网络提供了一个有吸引力的替代方案：可以从公共在线活动中估计实时感知，并与实体的通信进行比较，以量化公共消息传递如何影响感知。 虽然先前的算法方法纯粹依赖于基于文本的情感分析，但该项目将开发基于以下洞察力的新方法，即实体的在线社交联系表明它们是如何被感知的（例如，“物以类聚”）。因此，该项目将调查公众对实体的多个特征的看法，而不是典型的一维情感测量（例如，它是否被视为有利于环境、有利于健康等）。将进行多方面的评价，以研究“漂绿”现象，这是一种欺骗性的营销做法，公司在推销其产品或政策时，说它们比实际上更有利于环境。该项目有可能通过揭露欺骗性营销行为来加强对消费者的保护。该项目将开发社交网络分析算法，以评估对实体的感知，并开发语言处理算法，以量化实体在感知属性方面的通信。解决这两个问题的方法都依赖于创新的算法来衡量公共实体和代表感兴趣的感知属性的范例账户之间的社会和语言关系的强度。该方法的一个关键优点是它对人工输入的要求最低，例如，仅给定像“环境”这样的单个关键词，该方法识别合适的范例并且拟合语言和感知模型。该项目将开发新的机器学习方法，用于领域自适应，正未标记学习和从标签比例学习，以适应这些模型，并确保它们对遗漏的变量偏差具有鲁棒性。将使用Twitter和Facebook的公共数据对这些模型进行评估，以量化品牌和其他公共实体的认知和在线传播之间的关系，特别侧重于识别绿色清洗的案例。

Measuring public perceptions and how they change over time is a central problem in marketing, public health, and politics. Traditional measurement methods rely on surveys and focus groups, which can be costly and time-consuming. Online social networks offer an attractive alternative: real-time perceptions can be estimated from public, online activity and compared with an entity's communications to quantify how public messaging affects perception. While prior algorithmic approaches rely purely on text-based sentiment analysis, this project will develop novel methods based on the insight that an entity's online social connections are indicative of how they are perceived (e.g., "birds of a feather flock together"). Thus, rather than typical one-dimensional measures of sentiment, the project will instead investigate public perception with respect to multiple characteristics of an entity (e.g., is it seen as pro-environment, pro-health, etc.). A multi-faceted evaluation will be performed to study the phenomenon of "greenwashing," a deceptive marketing practice in which firms market their products or policies as more environmentally friendly than they truly are. This project has the potential to enhance consumer protection by exposing deceptive marketing practices.The project will develop social network analysis algorithms to assess perception of an entity and also language processing algorithms to quantify the communications of an entity with respect to a perceptual attribute. The approaches to both problems rely on innovative algorithms to measure the strengths of the social and linguistic relations between public entities and exemplar accounts that typify the perceptual attribute of interest. A key advantage of the approach is its minimal requirement of human input, e.g., given only a single keyword like "environment," the approach identifies suitable exemplars and fits linguistic and perceptual models. The project will develop novel machine learning methods for domain adaptation, positive-unlabeled learning, and learning from label proportions in order to fit such models and ensure they are robust to omitted variable bias. The models will be evaluated using public Twitter and Facebook data to quantify the relationship between the perceptions and online communications of brands and other public entities, with a particular focus on identifying cases of greenwashing.